The present invention is generally related to remote data collection and predictive analysis, and, more particularly, is related to a system and method for collecting and analyzing shipment parameter data affecting predicted statistical variables of articles being shipped in commerce.
As will be appreciated by those skilled in the art, it is desirable to be able to accurately predict statistical variables of articles that may be affected by environmental factors as the articles are being shipped from an originating point of shipment to a destination point, which may or may not be the final destination point of the article. Examples of such statistical variables may include article life expectancy, warranty costs, service and/or maintenance schedules, etc. For example, if it was feasible to reliably and accurately determine when a relatively expensive and delicate article, such as an X-ray tube, would fail, then one could arrange for just-in-time replacement of the article before the failure actually occurs. If one were to wait until the failure occurs, then there would be a downtime loss of service and a concomitant loss of revenue. Conversely, if one were to arbitrarily start a replacement of the article well in advance before the failure occurs, then there would be a monetary loss incurred by any resulting unused inventory. Thus, it would be desirable to provide a system and method that would allow for more accurately estimating the predicted statistical variables of the articles based on shipment parameter data indicative of various environmental factors to which the articles may have been subjected to during shipment.
It has been recently observed that various environmental parameters, e.g., ambient temperature, humidity, vibration, and other factors may have significant effects on the lifetime of key components of various electromechanical systems. In one exemplary case dealing with automobile dealer warranties, it was found that the Interior Climate and Control (ICC) system of an automobile was particularly sensitive to temperature extremes. It will be appreciated that the ICC system includes various electromechanical components, such as compressor, mounting bracket, clutch and pulley, orifice tube, condenser, heater core, heater control valve, receiver/dryer, evaporator, air ducts and outlets, accumulator, air conditioner temperature control program, seals and gaskets, etc. The dependence in this case was so marked that it was possible to create a substantially accurate state-by-state model for such dependence. FIG. 1 illustrates respective exemplary distribution graphs that represent ICC claims experienced in the state of Arizona as a function of month, which is in turn directly related to a function of peak ambient temperatures. As shown in FIG. 1, the distribution drawn in a solid line represents an empirically-derived distribution, and the distribution drawn in a dotted-line represents a distribution generated from a model that correlates peak temperatures versus ICC claims.
Thus, it is believed that environmental effects that occur during the shipment, e.g., transportation and/or storage of sensitive components, such as electromechanical parts, electronic tubes, integrated circuits, chemical materials, etc., may have significant impact on various predictive statistical variables of the articles being shipped. It is further believed that, if the appropriate shipment parameter data are collected and analyzed, a system and method may now be provided so as to reduce the variance of the predictive statistical variables of the respective articles, such as the estimated article lifetime, warranty costs, maintenance and/or servicing schedules, etc. It is desirable that such system and method allow for adjusting the predicted statistical variables of the respective articles based on the historical shipment data of such articles. It will be appreciated that such system and method would allow for substantially mitigating the above-discussed inventory and downtime losses.
Generally speaking, the present invention fulfills the foregoing needs by providing a system for collecting and analyzing shipment parameter data, e.g., temperature, vibration, acceleration, shock, humidity, barometric pressure, pH, transit time, container position, etc., affecting predicted statistical variables of articles. Examples of the predicted statistical variables may include article life expectancy, warranty costs, service and/or maintenance schedules, etc. The system comprises a plurality of data collection subsystems for respectively collecting shipment parameter data encountered by respective articles being shipped, and a data analysis subsystem coupled to receive the collected shipment data for adjusting the respective predicted statistical variables of the articles.
The present invention further fulfills the foregoing needs by providing a method for collecting and analyzing shipment parameter data affecting predicted statistical variables of articles. The method allows for collecting shipment parameter data encountered by articles being shipped using a plurality of respective data collection subsystems. The method further allows for adjusting the respective predicted statistical variables of the articles using a data analysis subsystem coupled to receive the collected shipment data.